{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/logit_4.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}18 Aug 2020, 08:58:29
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_raw.dta", clear
. {c )-}
{txt}
{com}. 
. if ("`model'" == "mi") {c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_imputed_M10.dta", clear  
. {c )-}
{txt}
{com}. 
. * for now, we use the following simple survey weights 
. if ("`model'" == "mi") {c -(}
.         mi svyset `geo_type' [pw=ObsWgt0] 
. {c )-}
{txt}
{com}. else {c -(}
.         svyset `geo_type' [pw=ObsWgt0]  

      {txt}pweight:{col 16}{res}ObsWgt0
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}county
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}
{com}. {c )-}
{txt}
{com}. 
. * margins for each category
. program margin_interact 
{txt}  1{com}.         args X Y k model
{txt}  2{com}.         sum `X', d 
{txt}  3{com}.         local gap = (`r(max)' - `r(min)') / `k' 
{txt}  4{com}.         if ("`model'" == "logit") {c -(}
{txt}  5{com}.                 margin `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  6{com}.         {c )-} 
{txt}  7{com}.         else if ("`model'" == "mi") {c -(}
{txt}  8{com}.                 mimrgns `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  9{com}.         {c )-}       
{txt} 10{com}. end 
{txt}
{com}. 
. program mchange_mi
{txt}  1{com}.         args X k model
{txt}  2{com}.         if ("`model'" == "logit") {c -(}
{txt}  3{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt}  4{com}.                         sum `X' if e(sample), d 
{txt}  5{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt}  6{com}.                         mlincom  2 - 1, decimal(7) stat(all)    
{txt}  7{com}.                 {c )-} 
{txt}  8{com}.                 else if ("`k'" == "binary") {c -(}
{txt}  9{com}.                         sum `X' if e(sample), d 
{txt} 10{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 11{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 12{com}.                 {c )-} 
{txt} 13{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 14{com}.                         margin `X' if e(sample)==1 , at() pwcompare predict(pr) 
{txt} 15{com}.                 {c )-}
{txt} 16{com}.         {c )-}
{txt} 17{com}. 
.         else if ("`model'" == "mi") {c -(}
{txt} 18{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt} 19{com}.                         sum `X' , d 
{txt} 20{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 21{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 22{com}.                 {c )-} 
{txt} 23{com}.                 else if ("`k'" == "binary") {c -(}
{txt} 24{com}.                         sum `X' , d 
{txt} 25{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 26{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 27{com}.                 {c )-} 
{txt} 28{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 29{com}.                         mimrgns `X' , at()   pwcompare predict(pr)      
{txt} 30{com}.                 {c )-}
{txt} 31{com}.         {c )-}
{txt} 32{com}. end
{txt}
{com}. 
. * create some dummy codings
. tab Race5, gen(race5_nh)

      {txt}Race5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  9,802,895       79.53       79.53
{txt}          2 {c |}{res}  1,330,104       10.79       90.32
{txt}          3 {c |}{res}    260,664        2.11       92.44
{txt}          4 {c |}{res}    250,301        2.03       94.47
{txt}          5 {c |}{res}    681,739        5.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,325,703      100.00
{txt}
{com}.         rename race5_nh1 White_nh 
{res}{txt}
{com}.         rename race5_nh2 Black_nh 
{res}{txt}
{com}.         rename race5_nh3 AIAN_nh 
{res}{txt}
{com}.         rename race5_nh4 AsPI_nh 
{res}{txt}
{com}.         rename race5_nh5 Hispanic
{res}{txt}
{com}. 
. tab MarStat5, gen(ms)

   {txt}MarStat5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  6,880,735       55.83       55.83
{txt}          2 {c |}{res}    924,215        7.50       63.32
{txt}          3 {c |}{res}  1,265,861       10.27       73.60
{txt}          4 {c |}{res}    237,203        1.92       75.52
{txt}          5 {c |}{res}  3,017,228       24.48      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,325,242      100.00
{txt}
{com}.         rename ms1 Marrd5
{res}{txt}
{com}.         rename ms2 Widow5
{res}{txt}
{com}.         rename ms3 Divor5
{res}{txt}
{com}.         rename ms4 Separ5
{res}{txt}
{com}.         rename ms5 NvMar5
{res}{txt}
{com}. 
. tab AgeGrp4, gen(ag) 

    {txt}AgeGrp4 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  1,930,142       15.66       15.66
{txt}          2 {c |}{res}  3,549,392       28.80       44.46
{txt}          3 {c |}{res}  4,304,660       34.92       79.38
{txt}          4 {c |}{res}  2,541,509       20.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,325,703      100.00
{txt}
{com}.         rename ag1 Age_15_24
{res}{txt}
{com}.         rename ag2 Age_25_44
{res}{txt}
{com}.         rename ag3 Age_45_64
{res}{txt}
{com}.         rename ag4 Age_65_Up
{res}{txt}
{com}. 
. destring St, replace 
{txt}St: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(16685 missing values generated)
{res}{txt}
{com}. 
. * set-up equations
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Cath Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local demographics_raw i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
{txt}
{com}. local demographics_same i.Female c.std_same_prop_Sex i.AgeGrp4 c.std_same_prop_AgeGrp4 i.Race5 c.std_same_prop_Race5 i.BornUSA c.std_same_prop_BornUSA i.MarStat5 c.std_same_prop_MarStat5 
{txt}
{com}. local demographics_inter i.Female##c.std_same_prop_Sex i.AgeGrp4##c.std_same_prop_AgeGrp4 i.Race5##c.std_same_prop_Race5 i.BornUSA##c.std_same_prop_BornUSA i.MarStat5##c.std_same_prop_MarStat5
{txt}
{com}. 
. 
. if (`model_version' == 1){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' 
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 
. {c )-}
{txt}
{com}. if (`model_version' == 2){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' i.UnEmpl c.RAT_UnEmpl i.PhysProb c.RAT_PhysProb
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 3){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' 
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 
. {c )-}
{txt}
{com}. if (`model_version' == 4){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' i.UnEmpl c.std_same_prop_UnEmpl i.PhysProb c.std_same_prop_PhysProb
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 UnEmpl std_same_prop_UnEmpl PhysProb std_same_prop_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 5){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' 
. {c )-}
{txt}
{com}. if (`model_version' == 6){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' i.UnEmpl##c.std_same_prop_UnEmpl i.PhysProb##c.std_same_prop_PhysProb
. {c )-}       
{txt}
{com}. * test how long it would take.
. * mi estimate: svy: mean Suic 
. * local demographics i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
. * mi estimate: svy: logit Suic i.St `demographics' UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. 
. * main effects : margins
. if ("`model'" == "mi"){c -(}
. 
.         mi estimate: svy: logit Suic i.St `model_eq', or 
.         estimates store m1 
. 
.         if (`model_version' <= 4){c -(}
.                 estimates restore m1
.                 mchange_mi Female "binary" "mi"
.                 estimates restore m1
.                 mchange_mi AgeGrp4 "categorical" "mi"
.                 estimates restore m1
.                 mchange_mi Race5 "categorical" "mi"
.                 estimates restore m1
.                 mchange_mi BornUSA "binary" "mi"
.                 estimates restore m1
.                 mchange_mi MarStat5 "categorical" "mi"
.                 
.                 if (`model_version' == 1 | `model_version' == 2) {c -(}
.                         estimates restore m1
.                         mchange_mi RAT_Female "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_5 "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 3 | `model_version' == 4) {c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_Sex "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_AgeGrp4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_Race5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_MarStat5 "continuous" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2 | `model_version' == 4){c -(}
.                         estimates restore m1
.                         mchange_mi UnEmpl "categorical" "mi"
.                         estimates restore m1
.                         mchange_mi PhysProb "categorical" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2){c -(}
.                         estimates restore m1
.                         mchange_mi RAT_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_PhysProb "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 4){c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_PhysProb "continuous" "mi"
.                 {c )-}               
.         {c )-}
. {c )-}
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         svy: logit Suic i.St `model_eq', or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 47}Number of obs{col 65}= {res}  11,587,123
{txt}{col 1}Number of PSUs{col 20}= {res}      918{txt}{col 47}Population size{col 65}={res}  411,228,041
{txt}{col 47}Design df{col 65}= {res}         917
{txt}{col 47}F({res}  51{txt},{res}    867{txt}){col 65}= {res}      472.34
{txt}{col 47}Prob > F{col 65}= {res}      0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        Suic{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}St {c |}
{space 10}8  {c |}{col 14}{res}{space 2} 1.117584{col 26}{space 2} .2023049{col 37}{space 1}    0.61{col 46}{space 3}0.539{col 54}{space 4} .7834151{col 67}{space 3} 1.594293
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .2120532{col 26}{space 2} .0431017{col 37}{space 1}   -7.63{col 46}{space 3}0.000{col 54}{space 4} .1422989{col 67}{space 3} .3160005
{txt}{space 9}21  {c |}{col 14}{res}{space 2}  .539839{col 26}{space 2} .1110381{col 37}{space 1}   -3.00{col 46}{space 3}0.003{col 54}{space 4} .3605383{col 67}{space 3} .8083083
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .3641311{col 26}{space 2} .0673643{col 37}{space 1}   -5.46{col 46}{space 3}0.000{col 54}{space 4} .2532662{col 67}{space 3} .5235261
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .1290968{col 26}{space 2} .0237282{col 37}{space 1}  -11.14{col 46}{space 3}0.000{col 54}{space 4} .0900029{col 67}{space 3} .1851716
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .3441714{col 26}{space 2} .0731885{col 37}{space 1}   -5.02{col 46}{space 3}0.000{col 54}{space 4} .2267391{col 67}{space 3} .5224242
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .9534412{col 26}{space 2} .1797831{col 37}{space 1}   -0.25{col 46}{space 3}0.800{col 54}{space 4} .6585326{col 67}{space 3} 1.380418
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .2616675{col 26}{space 2}  .048788{col 37}{space 1}   -7.19{col 46}{space 3}0.000{col 54}{space 4} .1814818{col 67}{space 3} .3772823
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .2694423{col 26}{space 2} .0553067{col 37}{space 1}   -6.39{col 46}{space 3}0.000{col 54}{space 4} .1801002{col 67}{space 3} .4031043
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .9505228{col 26}{space 2} .1612079{col 37}{space 1}   -0.30{col 46}{space 3}0.765{col 54}{space 4} .6814101{col 67}{space 3} 1.325917
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .2712353{col 26}{space 2} .0529204{col 37}{space 1}   -6.69{col 46}{space 3}0.000{col 54}{space 4} .1849481{col 67}{space 3} .3977798
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .2900453{col 26}{space 2} .0648168{col 37}{space 1}   -5.54{col 46}{space 3}0.000{col 54}{space 4} .1870663{col 67}{space 3}  .449714
{txt}{space 9}49  {c |}{col 14}{res}{space 2} 1.795846{col 26}{space 2}  .528423{col 37}{space 1}    1.99{col 46}{space 3}0.047{col 54}{space 4} 1.008032{col 67}{space 3} 3.199366
{txt}{space 9}51  {c |}{col 14}{res}{space 2}  1.15033{col 26}{space 2} .1937102{col 37}{space 1}    0.83{col 46}{space 3}0.406{col 54}{space 4} .8265982{col 67}{space 3} 1.600848
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .7481971{col 26}{space 2} .1319055{col 37}{space 1}   -1.65{col 46}{space 3}0.100{col 54}{space 4}  .529362{col 67}{space 3} 1.057497
{txt}{space 12} {c |}
{space 8}Year {c |}
{space 7}2006  {c |}{col 14}{res}{space 2} .9749306{col 26}{space 2} .0328464{col 37}{space 1}   -0.75{col 46}{space 3}0.451{col 54}{space 4} .9125528{col 67}{space 3} 1.041572
{txt}{space 7}2007  {c |}{col 14}{res}{space 2} 1.070467{col 26}{space 2} .0343292{col 37}{space 1}    2.12{col 46}{space 3}0.034{col 54}{space 4} 1.005171{col 67}{space 3} 1.140006
{txt}{space 7}2008  {c |}{col 14}{res}{space 2} 1.161371{col 26}{space 2} .0385598{col 37}{space 1}    4.51{col 46}{space 3}0.000{col 54}{space 4} 1.088108{col 67}{space 3} 1.239566
{txt}{space 7}2009  {c |}{col 14}{res}{space 2} 1.131912{col 26}{space 2} .0420697{col 37}{space 1}    3.33{col 46}{space 3}0.001{col 54}{space 4} 1.052287{col 67}{space 3} 1.217561
{txt}{space 7}2010  {c |}{col 14}{res}{space 2} 1.080611{col 26}{space 2} .0388405{col 37}{space 1}    2.16{col 46}{space 3}0.031{col 54}{space 4} 1.007011{col 67}{space 3} 1.159591
{txt}{space 7}2011  {c |}{col 14}{res}{space 2} 1.140756{col 26}{space 2} .0451867{col 37}{space 1}    3.32{col 46}{space 3}0.001{col 54}{space 4} 1.055434{col 67}{space 3} 1.232975
{txt}{space 12} {c |}
{space 4}1.Female {c |}{col 14}{res}{space 2} .2414759{col 26}{space 2}  .006619{col 37}{space 1}  -51.84{col 46}{space 3}0.000{col 54}{space 4} .2288289{col 67}{space 3} .2548219
{txt}std_same_p~x {c |}{col 14}{res}{space 2} .9762044{col 26}{space 2} .0137942{col 37}{space 1}   -1.70{col 46}{space 3}0.089{col 54}{space 4} .9495046{col 67}{space 3} 1.003655
{txt}{space 12} {c |}
{space 5}AgeGrp4 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 1.897932{col 26}{space 2} .0548364{col 37}{space 1}   22.18{col 46}{space 3}0.000{col 54}{space 4} 1.793307{col 67}{space 3} 2.008662
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 2.156293{col 26}{space 2} .0757193{col 37}{space 1}   21.88{col 46}{space 3}0.000{col 54}{space 4} 2.012695{col 67}{space 3} 2.310137
{txt}{space 10}4  {c |}{col 14}{res}{space 2} 1.762531{col 26}{space 2} .0821347{col 37}{space 1}   12.16{col 46}{space 3}0.000{col 54}{space 4} 1.608488{col 67}{space 3} 1.931326
{txt}{space 12} {c |}
std_same_p~4 {c |}{col 14}{res}{space 2} .9930325{col 26}{space 2} .0073755{col 37}{space 1}   -0.94{col 46}{space 3}0.347{col 54}{space 4} .9786626{col 67}{space 3} 1.007613
{txt}{space 12} {c |}
{space 7}Race5 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .3271484{col 26}{space 2} .0156591{col 37}{space 1}  -23.34{col 46}{space 3}0.000{col 54}{space 4}  .297816{col 67}{space 3} .3593699
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .9117045{col 26}{space 2} .1039094{col 37}{space 1}   -0.81{col 46}{space 3}0.418{col 54}{space 4} .7289743{col 67}{space 3} 1.140239
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .6872535{col 26}{space 2} .0555665{col 37}{space 1}   -4.64{col 46}{space 3}0.000{col 54}{space 4} .5864134{col 67}{space 3} .8054343
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4269895{col 26}{space 2} .0260344{col 37}{space 1}  -13.96{col 46}{space 3}0.000{col 54}{space 4} .3788342{col 67}{space 3}  .481266
{txt}{space 12} {c |}
std_same_~e5 {c |}{col 14}{res}{space 2} 1.005473{col 26}{space 2} .0163569{col 37}{space 1}    0.34{col 46}{space 3}0.737{col 54}{space 4} .9738782{col 67}{space 3} 1.038092
{txt}{space 3}1.BornUSA {c |}{col 14}{res}{space 2} 1.306161{col 26}{space 2} .0783919{col 37}{space 1}    4.45{col 46}{space 3}0.000{col 54}{space 4} 1.161027{col 67}{space 3} 1.469436
{txt}std_same_p~A {c |}{col 14}{res}{space 2} .9804979{col 26}{space 2} .0121502{col 37}{space 1}   -1.59{col 46}{space 3}0.112{col 54}{space 4} .9569401{col 67}{space 3} 1.004636
{txt}{space 12} {c |}
{space 4}MarStat5 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 1.968319{col 26}{space 2} .0621559{col 37}{space 1}   21.44{col 46}{space 3}0.000{col 54}{space 4} 1.850038{col 67}{space 3} 2.094163
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 2.622912{col 26}{space 2} .0510179{col 37}{space 1}   49.58{col 46}{space 3}0.000{col 54}{space 4} 2.524673{col 67}{space 3} 2.724973
{txt}{space 10}4  {c |}{col 14}{res}{space 2} 1.256636{col 26}{space 2} .1284105{col 37}{space 1}    2.24{col 46}{space 3}0.026{col 54}{space 4} 1.028286{col 67}{space 3} 1.535696
{txt}{space 10}5  {c |}{col 14}{res}{space 2} 1.897333{col 26}{space 2}  .048714{col 37}{space 1}   24.94{col 46}{space 3}0.000{col 54}{space 4} 1.804098{col 67}{space 3} 1.995387
{txt}{space 12} {c |}
std_same_~t5 {c |}{col 14}{res}{space 2}  .990063{col 26}{space 2}  .008373{col 37}{space 1}   -1.18{col 46}{space 3}0.238{col 54}{space 4} .9737661{col 67}{space 3} 1.006633
{txt}{space 1}Rat_Poverty {c |}{col 14}{res}{space 2} .9905619{col 26}{space 2} .0039126{col 37}{space 1}   -2.40{col 46}{space 3}0.017{col 54}{space 4} .9829129{col 67}{space 3} .9982705
{txt}{space 1}Rat_Mig_Cum {c |}{col 14}{res}{space 2} 1.648004{col 26}{space 2} 1.872568{col 37}{space 1}    0.44{col 46}{space 3}0.660{col 54}{space 4} .1772108{col 67}{space 3} 15.32592
{txt}{space 5}Pop_Den {c |}{col 14}{res}{space 2}  .999997{col 26}{space 2} .0000146{col 37}{space 1}   -0.20{col 46}{space 3}0.838{col 54}{space 4} .9999684{col 67}{space 3} 1.000026
{txt}{space 1}Rat_GC_ProE {c |}{col 14}{res}{space 2} .9999519{col 26}{space 2} .0000234{col 37}{space 1}   -2.06{col 46}{space 3}0.040{col 54}{space 4}  .999906{col 67}{space 3} .9999978
{txt}{space 1}Rat_GC_ProM {c |}{col 14}{res}{space 2} 1.000051{col 26}{space 2} .0000238{col 37}{space 1}    2.13{col 46}{space 3}0.033{col 54}{space 4} 1.000004{col 67}{space 3} 1.000097
{txt}{space 1}Rat_GC_ProB {c |}{col 14}{res}{space 2} 1.000037{col 26}{space 2} .0001326{col 37}{space 1}    0.28{col 46}{space 3}0.782{col 54}{space 4} .9997765{col 67}{space 3} 1.000297
{txt}{space 2}Rat_GC_Jew {c |}{col 14}{res}{space 2} 1.000179{col 26}{space 2}  .000081{col 37}{space 1}    2.21{col 46}{space 3}0.027{col 54}{space 4}  1.00002{col 67}{space 3} 1.000338
{txt}{space 2}Rat_GC_Oth {c |}{col 14}{res}{space 2} .9999337{col 26}{space 2} .0000355{col 37}{space 1}   -1.87{col 46}{space 3}0.062{col 54}{space 4} .9998641{col 67}{space 3} 1.000003
{txt}{space 4}1.UnEmpl {c |}{col 14}{res}{space 2} 6.037762{col 26}{space 2} .3048116{col 37}{space 1}   35.62{col 46}{space 3}0.000{col 54}{space 4} 5.468233{col 67}{space 3}  6.66661
{txt}std_same_p~l {c |}{col 14}{res}{space 2} .9591874{col 26}{space 2} .0209163{col 37}{space 1}   -1.91{col 46}{space 3}0.056{col 54}{space 4} .9190039{col 67}{space 3} 1.001128
{txt}{space 2}1.PhysProb {c |}{col 14}{res}{space 2} 1.502649{col 26}{space 2} .0469266{col 37}{space 1}   13.04{col 46}{space 3}0.000{col 54}{space 4} 1.413319{col 67}{space 3} 1.597626
{txt}std_same_p~b {c |}{col 14}{res}{space 2} .8720633{col 26}{space 2} .0131558{col 37}{space 1}   -9.07{col 46}{space 3}0.000{col 54}{space 4} .8466228{col 67}{space 3} .8982683
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0000626{col 26}{space 2} .0000136{col 37}{space 1}  -44.65{col 46}{space 3}0.000{col 54}{space 4} .0000409{col 67}{space 3} .0000958
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}
{com}.         estimates store m1 
. 
.         if (`model_version' <= 4){c -(}
.                 mchange `margin_demographics', amount(all) delta(100) statistics(all) decimals(7)

{res}svy logit: Changes in Pr(y) | Number of obs = 11525152

{txt}Expression: Pr(Suic), predict(pr)
{res}
{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Change}{space 1}{space 1}{ralign 9:p-value}{space 1}{space 1}{ralign 9:LL}{space 1}{space 1}{ralign 9:UL}{space 1}{space 1}{ralign 9:z-value}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Female}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000855}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000894}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000817}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.36e+01}}}{space 1}
{space 0}{res:{lalign 13:std same p~x}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000016}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0861885}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000035}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.72e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000016}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0866374}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000035}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.72e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000628}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000805}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000451}}}{space 1}{space 1}{ralign 9:{res:{sf:-6.95e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000121}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0908178}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000262}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.69e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000017}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0904496}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000036}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000003}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.69e+00}}}{space 1}
{space 0}{res:{lalign 13:AgeGrp4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000353}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000324}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000382}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.37e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000454}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000417}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000491}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.42e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000300}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000251}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000348}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.21e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000101}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000071}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000132}}}{space 1}{space 1}{ralign 9:{res:{sf:6.5886967}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000053}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0272635}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000100}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.21e+00}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000155}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000193}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000117}}}{space 1}{space 1}{ralign 9:{res:{sf:-7.97e+00}}}{space 1}
{space 0}{res:{lalign 13:std same p~4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3465268}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.9418317}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3458909}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.9430750}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000347}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1744814}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000848}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000154}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.36e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000051}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3477905}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000156}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000055}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.9393654}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3475726}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.9397904}}}{space 1}
{space 0}{res:{lalign 13:Race5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000549}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000586}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000511}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.87e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000072}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3946134}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000238}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000094}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.8516812}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000255}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000001}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000348}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000162}}}{space 1}{space 1}{ralign 9:{res:{sf:-5.41e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000467}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000518}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000417}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.81e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000477}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000299}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000654}}}{space 1}{space 1}{ralign 9:{res:{sf:5.2749534}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000294}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000215}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000372}}}{space 1}{space 1}{ralign 9:{res:{sf:7.3570848}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000081}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000114}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000045}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000118}}}{space 1}{space 1}{ralign 9:{res:{sf:4.4140515}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000183}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0928541}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000397}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000030}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.68e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000395}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000239}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000578}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000213}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.25e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000212}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000288}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000137}}}{space 1}{space 1}{ralign 9:{res:{sf:-5.52e+00}}}{space 1}
{space 0}{res:{lalign 13:std same p~5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.7377336}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000026}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3349577}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.7380708}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000026}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3345106}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000501}}}{space 1}{space 1}{ralign 9:{res:{sf:0.7960161}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0003299}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0004300}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2585816}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000046}}}{space 1}{space 1}{ralign 9:{res:{sf:0.7405824}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000229}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000321}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3311824}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.7373824}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000026}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3354234}}}{space 1}
{space 0}{res:{lalign 13:BornUSA}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000165}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000100}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000230}}}{space 1}{space 1}{ralign 9:{res:{sf:4.9621788}}}{space 1}
{space 0}{res:{lalign 13:std same p~A}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1053922}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000029}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000003}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.62e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1094485}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000030}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000003}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.60e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000594}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000010}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000830}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000358}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.93e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000209}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1116572}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000468}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000049}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.59e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000014}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1129587}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000030}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000003}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.59e+00}}}{space 1}
{space 0}{res:{lalign 13:MarStat5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000451}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000393}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000509}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.54e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000756}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000709}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000802}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.19e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000120}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0424139}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000235}}}{space 1}{space 1}{ralign 9:{res:{sf:2.0322724}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000418}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000380}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000456}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.14e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000305}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000241}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000368}}}{space 1}{space 1}{ralign 9:{res:{sf:9.4380390}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000332}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000465}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000199}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.89e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000033}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3447540}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000102}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000036}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.9453015}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000636}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000760}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000513}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.01e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000338}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000387}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000289}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.35e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000298}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000001}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000189}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000408}}}{space 1}{space 1}{ralign 9:{res:{sf:5.3431520}}}{space 1}
{space 0}{res:{lalign 13:std same p~5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000007}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2337716}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.19e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000007}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2329727}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.19e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000436}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0410197}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000854}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.05e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000067}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2357937}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000178}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000044}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.19e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000007}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2353195}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.19e+00}}}{space 1}
{space 0}{res:{lalign 13:UnEmpl}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0002775}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002524}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0003025}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.17e+01}}}{space 1}
{space 0}{res:{lalign 13:std same p~l}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000028}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0522827}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000057}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.94e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000028}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0517661}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000057}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.95e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000679}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000732}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000627}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.54e+01}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000265}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0623393}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000544}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000014}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.87e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000029}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0567753}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000058}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000001}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.91e+00}}}{space 1}
{space 0}{res:{lalign 13:PhysProb}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000320}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000263}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000377}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.09e+01}}}{space 1}
{space 0}{res:{lalign 13:std same p~b}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000094}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000115}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000074}}}{space 1}{space 1}{ralign 9:{res:{sf:-8.93e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000088}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000106}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000070}}}{space 1}{space 1}{ralign 9:{res:{sf:-9.50e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000715}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000666}}}{space 1}{space 1}{ralign 9:{res:{sf:-5.52e+01}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000713}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000891}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000535}}}{space 1}{space 1}{ralign 9:{res:{sf:-7.88e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000094}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000115}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000074}}}{space 1}{space 1}{ralign 9:{res:{sf:-8.89e+00}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Std Err}{space 1}{space 1}{ralign 9:From}{space 1}{space 1}{ralign 9:To}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Female}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000020}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001128}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000272}}}{space 1}
{space 0}{res:{lalign 13:std same p~x}}{c |}{space 11}{space 11}{space 11}
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{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000010}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000674}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000090}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000062}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000072}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000752}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000631}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000010}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:AgeGrp4}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000393}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000746}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000393}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000847}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000025}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000393}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000693}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000746}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000847}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000024}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000746}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000693}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000847}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000693}}}{space 1}
{space 0}{res:{lalign 13:std same p~4}}{c |}{space 11}{space 11}{space 11}
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{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000685}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000255}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000343}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000054}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000716}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000665}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:Race5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000816}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000267}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000085}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000816}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000744}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000047}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000816}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000561}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000026}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000816}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000348}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000090}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000267}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000744}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000040}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000267}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000561}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000267}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000348}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000109}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000744}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000561}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000093}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000744}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000348}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000038}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000561}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000348}}}{space 1}
{space 0}{res:{lalign 13:std same p~5}}{c |}{space 11}{space 11}{space 11}
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{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000694}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001936}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001191}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000140}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000676}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000722}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:BornUSA}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000033}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000538}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000703}}}{space 1}
{space 0}{res:{lalign 13:std same p~A}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000682}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000669}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000677}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000120}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000096}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000132}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000795}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000585}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:MarStat5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000029}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000466}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000917}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000024}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000466}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001222}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000059}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000466}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000586}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000020}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000466}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000884}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000032}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000917}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001222}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000068}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000917}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000586}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000035}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000917}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000884}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000063}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001222}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000586}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000025}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001222}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000884}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000056}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000586}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000884}}}{space 1}
{space 0}{res:{lalign 13:std same p~5}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000691}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000684}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000683}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000213}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000254}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000056}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000725}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000658}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:UnEmpl}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000128}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000551}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0003326}}}{space 1}
{space 0}{res:{lalign 13:std same p~l}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000692}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000664}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000014}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000662}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000027}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000142}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000843}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000578}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000015}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:PhysProb}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000029}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000637}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000957}}}{space 1}
{space 0}{res:{lalign 13:std same p~b}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000738}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000643}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000602}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000091}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001176}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000463}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{res}
{txt}{p 0 0 2}Average predictions{p_end}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:0}{space 1}{space 1}{ralign 9:1}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Pr(y|base)}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.9999310}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000690}}}{space 1}

{col 1}1: Delta equals 100.
{com}.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. if (`model_version' == 5 | `model_version' == 6) {c -(}
.         estimates restore m1
.         margin_interact std_same_prop_Sex Female 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_AgeGrp4 AgeGrp4 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_Race5 Race5 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_BornUSA BornUSA 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_MarStat5 MarStat5 5 `model'
. 
.         if (`model_version' == 6) {c -(}
.                 estimates restore m1
.                 margin_interact std_same_prop_UnEmpl UnEmpl 5 `model'
.                 estimates restore m1
.                 margin_interact std_same_prop_PhysProb PhysProb 5 `model'
.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. 
. log close 
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/logit_4.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}18 Aug 2020, 10:19:16
{txt}{.-}
{smcl}
{txt}{sf}{ul off}